Description:Title of The Invention
AN AUTO MONITORING EXAMINATION SYSTEM FOR RECOGNITION OF ABNORMAL ACTIVITY USING DEEP LEARNING AND IOT DEVICES
Field of the Invention
This invention relates to an auto monitoring examination system for recognition of abnormal activity using deep learning and IOT devices
Background of the Invention
US9680852B1 says that Computer-implemented methods and apparatuses for recursive multi-layer examination for computer network security remediation is provided herein. Exemplary methods may include: receiving a first identifier associated with a first node; retrieving first metadata using the first identifier; identifying a second node in communication with the first node using the first metadata; ascertaining a first characteristic of each first communication between the first and second nodes using the first metadata; examining each first communication for malicious behavior using the first characteristic; receiving a first risk score for each first communication responsive to the examining; determining the first risk score associated with one of the second communications exceeds a first predetermined threshold and indicating the first and second nodes are malicious. Exemplary methods may further include providing the identified malicious nodes and communications originating from or directed to the malicious nodes.
Research Gap: Exiting system ensure security of computer network for online examination only. Proposed work ensures the secure system for offline examination without invigilator.
WO2022097805A1 says that the present invention relates to a method, device, and system for detecting an abnormal event. The method for detecting an abnormal event, according to one embodiment of the present invention, is a method which is performed by an electronic device and detects an abnormal event for an object in a Realtime 3D depth image captured by a 3D depth camera of an image surveillance system, the method comprising the steps of: acquiring, in realtime, a 3D depth image and Internet of things (IoT) sensor data around a specific location; recognizing, on the basis of the acquired 3D depth image, whether there is an abnormal event for an object in the image by using an event recognition model pre-trained according to a deep learning technique, using training data comprising input data for the 3D depth image and result data about whether there is an abnormal event or the type of abnormal event; and, if an event is recognized as abnormal, determining whether an abnormal event has actually occurred by using the IoT sensor data.
Research Gap: The proposed secure examination system for auto monitoring of abnormal activities of candidates in the examination room. Detection of abnormal activities of candidate is captured by the high-definition camera, further processed with an image processing model based on neural network.
CN106662746B says that headgear (10) comprising: a frame (22) having a support portion supported on the head of the examinee; an imaging device (14) for obtaining images of the surroundings and the front environment of said examinee taking part; a biometric identification system (26) for obtaining biometric data about a person, a display (12) viewable by an examiner, and a sound generator and detector (16, 18). A processor on the frame (22) is coupled to these components and controls the content of the display (12) based on input received via the imaging device (14) and biometric system (26). The processor also monitors the detection of audio communications by the sound detector (16) relative to sounds produced by the sound generator (18) and images obtained by the imaging device (14) and displays the test questions on the display (12) to determine whether a person other than the examinee is present in the images obtained by the imaging device (14) or to provide verbal information to the examinee.
Research Gap: The automating Examination system captures the video through multiple edge devices situated at each examination rooms there is no physical assistance required for observation, if any abnormal detection has taken place, the IOT based alarm system will produce an alert sound.
None of the prior art indicate above either alone or in combination with one another disclose what the present invention has disclosed.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
Disclosed herein an Auto Monitoring Examination System for Recognition of Abnormal Activity Using Deep Learning and IoT Devices comprises Edge Computing Device (101), Video Capture (102), Edge Assisted Vision Devices (103), Computing Unit (Rasberry Pi) (10), Database, TFT/LCD Screen (11), HD Camera (12), Card (13), Mouse (14), Keyboard (15), Neural Stick (16), Trained Models (17) and Co-processor (18).
In another embodiment Monitoring the abnormal activity of candidates in the exam centres during examination is very important, but it is so costly in a place like India, installing a CCTV camera would not be beneficial, using CCTV cameras could be inefficient as they only reveal defrauder’s faces, is not be effective in detecting abnormal activities.
In another embodiment in order to address this, a person must be hired to watch the CCTV camera; to overcome this problem, a more advanced auto-monitoring system based on deep learning and IOT-based technologies that detects abnormal activities of a candidate using image processing; in this system, a web monitors each various classroom with a camera on the edge device;
In another embodiment Video Capturing; The deep learning CNN algorithm runs through taking inputs from camera it takes the video and converts it into the frames.
In another embodiment Video Storage; A large area is typically watched over or monitored by a number of CCTVs; we used a run-time video recording system to capture real-time videos into the server.
In another embodiment Video Preprocessing; The initial step in this method is to extract frames from the CCTV recordings that were obtained; image processing classification has been done with the use of deep learning neural models; convolution Neural Network is known for its effectiveness in recognizing and classifying images; object detections, face recognition etc., are some of the areas where CNNs have a wide scope and hence rightly used; a CNN takes in an input image, assigns weights to different objects and features in an image and thereby recognizes in this manner.
In another embodiment CNN is a classification algorithm and the processing needed before execution is extremely low. CNN has the ability to learn features on its own after training the model for a while.
In another embodiment Frames Extraction; In this process we detect abnormal frames having abnormal behavior using deep learning CNN Monitoring System.
In another embodiment Auto Messaging; This component of proposed system will automatically generate a message on every abnormal activity detected to the IOT Devices.
In another embodiment Trigger IoT Devices; As an abnormal activity detected IOT devices triggered at different class rooms; Real-time monitoring is one of the useful functions provided by IoT technology to provide real-time support when an abnormal activity is observed; the convolutional neural network identifies the various faces captured by the edge assisted vision device (GPS + Camera + RTC). Results are sent to the cloud server's database for sending response and for future reference.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
Figure 1: Flow Chart
Figure 2: Flow Chart
Figure 3: Flow Chart
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Discloses herein An Auto Monitoring Examination System for Recognition of Abnormal Activity Using Deep Learning and IoT Devices comprises Edge Computing Device (101), Video Capture (102), Edge Assisted Vision Devices (103), Computing Unit (Rasberry Pi) (10), Database, TFT/LCD Screen (11), HD Camera (12), Card (13), Mouse (14), Keyboard (15), Neural Stick (16), Trained Models (17) and Co-processor (18).
METHOD OF WORKING
Monitoring the abnormal activity of candidates in the exam centers during examination is very important, but it is so costly in a place like India, installing a CCTV camera would not be beneficial, using CCTV cameras could be inefficient as they only reveal defrauder’s faces, will not be effective in detecting abnormal activities; in order to address this, a person must be hired to watch the CCTV camera. To overcome this problem, we introduced a more advanced auto-monitoring system based on deep learning and IOT-based technologies that can detect abnormal activities of a candidate using image processing. In this system, a web monitors each various classroom with a camera on the edge device.
Work flow of Proposed system
Video Capturing(101): The deep learning CNN algorithm runs through taking inputs from camera it takes the video and converts it into the frames.

Video Storage: A large area is typically watched over or monitored by a number of CCTVs. We used a run-time video recording system to capture real-time videos into the server.
Video Preprocessing: The initial step in this method is to extract frames from the CCTV recordings that were obtained. Image processing classification has been done with the use of deep learning neural models. Convolution Neural Network is known for its effectiveness in recognizing and classifying images. Object detections, face recognition etc., are some of the areas where CNNs have a wide scope and hence rightly used. A CNN takes in an input image, assigns weights to different objects and features in an image and thereby recognizes in this manner. CNN is a classification algorithm and the processing needed before execution is extremely low. CNN has the ability to learn features on its own after training the model for a while.
Frames Extraction: In this process we detect abnormal frames having abnormal behavior using deep learning CNN Monitoring System.
Auto Messaging: This component of proposed system will automatically generate a message on every abnormal activity detected to the IOT Devices
Trigger IoT Devices: As an abnormal activity detected IOT devices triggered at different class rooms. Real-time monitoring is one of the useful functions provided by IoT technology to provide real-time support when an abnormal activity is observed
The convolutional neural network identifies the various faces captured by the edge assisted vision device (GPS + Camera + RTC). Results are sent to the cloud server's database for sending response and for future reference.
ADVANTAGES OF THE INVENTION:
1. In this system candidate is automatically monitored in the examination room.
2. No Human intervention needed.
3. Abnormal activity of candidate is observed by the CCTV cameras.
4. All abnormal activities in the video fragment are validated with the help of Deep Learning Neural Networks based Model and send a message to the IoT alarming system.
5. On Positive result of abnormal activity an IoT based alarm system is triggered.
6. Proposed System ensure security of computer network for online examination only.
7. Proposed work ensures the secure system for offline examination without invigilator.
8. The proposed secure examination system for auto monitoring of abnormal activities of candidates in the examination room.
, Claims:
1. An Auto Monitoring Examination System for Recognition of Abnormal Activity Using Deep Learning and IoT Devices comprises Edge Computing Device (101), Video Capture (102), Edge Assisted Vision Devices (103), Computing Unit (Rasberry Pi) (10), Database, TFT/LCD Screen (11), HD Camera (12), Card (13), Mouse (14), Keyboard (15), Neural Stick (16), Trained Models (17) and Co-processor (18).
2. The system as claimed in claim 1, wherein Monitoring the abnormal activity of candidates in the exam centers during examination is very important, but it is so costly in a place like India, installing a CCTV camera would not be beneficial, using CCTV cameras could be inefficient as they only reveal defrauder’s faces, is not be effective in detecting abnormal activities.
3. The system as claimed in claim 1, wherein in order to address this, a person must be hired to watch the CCTV camera; to overcome this problem, a more advanced auto-monitoring system based on deep learning and IOT-based technologies that detects abnormal activities of a candidate using image processing; in this system, a web monitors each various classroom with a camera on the edge device;
4. The system as claimed in claim 1, wherein Video Capturing; The deep learning CNN algorithm runs through taking inputs from camera it takes the video and converts it into the frames.
5. The system as claimed in claim 1, wherein Video Storage; A large area is typically watched over or monitored by a number of CCTVs; we used a run-time video recording system to capture real-time videos into the server.
6. The system as claimed in claim 1, wherein Video Preprocessing; The initial step in this method is to extract frames from the CCTV recordings that were obtained; image processing classification has been done with the use of deep learning neural models; convolution Neural Network is known for its effectiveness in recognizing and classifying images; object detections, face recognition; and are some of the areas where CNNs have a wide scope and hence rightly used; a CNN takes in an input image, assigns weights to different objects and features in an image and thereby recognizes in this manner.
7. The system as claimed in claim 1, wherein CNN is a classification algorithm and the processing needed before execution is extremely low; and CNN has the ability to learn features on its own after training the model for a while.
8. The system as claimed in claim 1, wherein Frames Extraction; In this process we detect abnormal frames having abnormal behaviour using deep learning CNN Monitoring System.
9. The system as claimed in claim 1, wherein 1, Auto Messaging; This component of proposed system will automatically generate a message on every abnormal activity detected to the IOT Devices.
10. The system as claimed in claim 1, wherein Trigger IoT Devices; As an abnormal activity detected IOT devices triggered at different class rooms; Real-time monitoring is one of the useful functions provided by IoT technology to provide real-time support when an abnormal activity is observed; the convolutional neural network identifies the various faces captured by the edge assisted vision device (GPS + Camera + RTC). Results are sent to the cloud server's database for sending response and for future reference.